There are several technologies currently available which are designed to create more personal, efficient work and/or pleasure environments for humans. The best known technologies are listed here along with the problems inherent in them:    1—Human Computer Interaction (HCI) Design: This technology is an outgrowth of the ergonomic engineering efforts of the 1960's through 70's and functions by bringing descriptions of the audience and proposed functionality into focus. It can be broken down into: 1) describe the characteristics of the potential user population(s) via demographics, domain knowledge, etc.; 2) determine the goals the user(s) wants to accomplish; 3) identify the tasks that the users perform (in task language); 4) analyze the tasks to develop layers of sub-tasks (all of this should be in terms of tasks, not actions); 5) gather as much information as you can for design and later testing; 6) brainstorm about your user population and the relevant tasks they perform; and 7) draft a User/Task Table. Each of these stages can be broken down into detailed substages.
This technology is useful for determining characteristics of large groups only. It requires larger and larger sample populations of the desired demographic to create a model with any degree of accuracy. While not a flaw, per se, this technology is predicated on first defining a viable demographic model. This requires first defining characteristics of the demographic, said characteristics are themselves based on assumptions which may or may not be valid as they are based on observations of inconsistencies in the general population. These inconsistencies become the characteristics of the desired demographic, and so on for further refinements of the population.    2—Heuristic Design: This technology involves an on-going series of experimental, often trial-and-error, methods wherein a given design or system is continually modified based on field analysis, surveys, unsolicited feedback, etc. In most cases this technology requires users to be actively monitored or interrogated about their usage and/or activity in the subject environment. Data collected by such means is then compiled and correlated to possible design changes which are then either included or discarded based on decisions made without the continued input of the original users. Often any implemented changes are not injected into the subject environment unless some time after the original study, at which point the original users have themselves changed and the newly implemented changes are no longer relevant.    3—Bayesian Analysis: The simplest explanation of Bayesian Analysis is to say that it is statistics weighted by experience. While not useful in situations with exact binary outcomes, it is very useful in situations where not all the variables are known or defined but in which the final state of those variables is well known and defined. Its usefulness in these latter situations is based on its ability to infer preferred (ie, “experienced”) final states and then determine the statistical probability of the preferred state without knowing the statistical probability of the underlying states. Thus, statistics may tell us that state 2 has a 1/25 chance of being the final state of a 25 state system, but if experience tells us state 2 occurs 80% of the time, a simplified Bayesian Analysis would suggest that state 2 actually has an 80% chance of occurring again. A key element of Bayesian Analysis is that prior beliefs must be stated and well-defined because they will greatly affect the outcome of the analysis. Problems with technologies based on this method involve the requirement of prior beliefs and experience with which to weight the statistical probabilities, the accuracy and validity of these prior beliefs and experiences, there is no attempt to measure the goodness of the curve's fit to real results, and the end results of the analysis may rely more on the prior weighting measurements than the actual data itself.    4—Aggregators: The most popular of all existing technologies, Aggregators work simply by collecting as much data as possible then mining it for patterns which have ended in desired results. Typical Aggregators group people demographically (zip code is the most obvious), financially (income), by spending habits (buying patterns), and the like. All Aggregators work by determining what an individual will do based on what large groups of similarly employed, similarly housed, etc., individuals have done. The quality of the aggregate result is therefore based on larger and larger population models and the belief that “all individuals who meet this criteria behave in this way” (see Bayesian Analysis above). The problem with Aggregators is their inability to adjust to individual behaviors on the fly, to learn from immediate and near-past experience, to utilize that learning in real-time, and to evolve their methods as the populations evolve.    5—User Analysis: User Analysis is a multi-step technology which starts with a definition of purpose then moves through definition of goal or accomplishment, definition of audience, determination of strategy, determination of representative users, testing with said users, redefinition of purpose, goal or accomplishment, to creation of information and/or content. Substages include surveying potential users, interviews, task analysis, and the use of focus groups. In short, User Analysis is market analysis refined. The major problem with this technology is that the user, except in focus groups or in representational studies, has no control over the presentation or content they are delivered. In focus groups and representational studies people are in controlled and constrained environments where they are not free to be themselves and act out their inner attitudes based on whims of the moment.    6—User Centered Design: This technology tends to use the very latest behavioral and perceptual analysis tools to develop models of user behavior, often with exhaustive user testing for the ultimate in usage optimization. The “user testing” mentioned above takes the form of highly invasive methods such as having users wear hardware to monitor their brain activity, pulse, respiration, eye movement, and so on. In many ways User Centered Design is a polygraph method of determining user needs, wants and desires. Based on this kind of testing, decisions for design and implementation are made. Flaws are inherent in the description above. Design and implementation decisions are made based on controlled, constrained, contrived and confined experimentation over a large group of users in an unnatural setting. The end result of these tests are inherently invalid unless an adequate model of the unnatural setting skewing factor can be validated and taken into account.    7—Usage Analysis: This technology monitors how something is used in order to make it more usable. Usage Analysis is a combination of several of the methods already presented (Aggregator, Bayesian, HCI and User Design). User Analysis attempts to create aggregate data across as many platforms and transactions as possible. The more discrete forms of this technology rely more on Aggregation and Bayesian or similar forms of analysis rather than HCI and User Design methods. In all cases, Usage Analysis relies heavily on past usage to determine present and future usage. An example of this is using a table-knife for a screwdriver, or “necessity is the mother of invention”. Because Usage Analysis would expend its efforts watching how the table-knife was used as a table-knife to make a better table-knife, no time would be spent appreciating that the basic form of the table-knife is a close approximation to the basic form of a screwdriver, hence a one-way substitution could be made. As with previously mentioned technologies, usage analysis creates a constrained environment in which analysis occurs. This constrained environment does not reveal the complete range of interactions between the user and the environment or tool being used.